Detection of African horse sickness virus in Culicoides imicola pools using RT‐qPCR

African horse sickness (AHS) is an infectious, non‐contagious arthropod‐borne disease of equids, caused by the African horse sickness virus (AHSV), an orbivirus of the Reoviridae family. It is endemic in sub‐Saharan Africa and thought to be the most lethal viral disease of horses. This study focused on detection of AHSV in Culicoides imicola (Diptera: Ceratopogonidae) pools by the application of a RT‐qPCR. Midges were fed on AHSV‐infected blood. A single blood‐engorged female was allocated to pools of unfed nulliparous female midges. Pool sizes varied from 1 to 200. RNA was extracted and prepared for RT‐qPCR. The virus was successfully detected and the optimal pool size for the limit of detection of the virus was determined at a range between 1 to 25. Results from this investigation highlight the need for a standardized protocol for AHSV investigation in Culicoides midges especially for comparison among different studies and for the determination of infection rate.     

Use of the score test as a goodness-of-fit measure of the covariance structure in genetic analysis of longitudinal data

Model selection is an essential issue in longitudinal data analysis since many different models have been proposed to fit the covariance structure. The likelihood criterion is commonly used and allows to compare the fit of alternative models. Its value does not reflect, however, the potential improvement that can still be reached in fitting the data unless a reference model with the actual covariance structure is available. The score test approach does not require the knowledge of a reference model, and the score statistic has a meaningful interpretation in itself as a goodness-of-fit measure. The aim of this paper was to show how the score statistic may be separated into the genetic and environmental parts, which is difficult with the likelihood criterion, and how it can be used to check parametric assumptions made on variance and correlation parameters. Selection of models for genetic analysis was applied to a dairy cattle example for milk production.